Customizing recipe recommendations

ABSTRACT

A method for customizing recipe recommendations may include receiving a target number of calories for a user, receiving physical movement data of the user from one or more electronic sensors configured to directly measure physical movement of the user, analyzing the received physical movement data, determining one or more physical movement parameters based on the analysis of the received physical movement data, determining the recentness of each of the recipes being recommended or logged to the user, assigning a weight to each of the recipes based on the received target number of calories for the user, the determined one or more physical movement parameters, and the determined recentness of the recipe being recommended or logged to the user, ranking the recipes based on their assigned weights, and generating a custom recipe recommendation for the user based on the ranking of the recipes.

RELATED APPLICATIONS

This application claims priority to U.S. patent application Ser. No.62/400,773 titled “Customizing Recipe Recommendations” and filed on 28Sep. 2016, which application is herein incorporated by reference for allthat it discloses.

BACKGROUND

Recipes for nutritious meals are used by many consumers in an effort toachieve a healthy diet. There are perhaps millions or even billions ofdifferent recipes available in various publications such as recipebooks, magazines, health books, and online recipe databases.

One common problem faced by consumers of recipes is selecting anappropriate recipe from among the overwhelming number of choicesavailable. One way a consumer may deal with this problem is to consultwith a dietitian, who may make a recommendation of one or more recipesbased on an analysis of the consumer's preferences and healthy andunhealthy habits. However, such a consultation can be expensive, timeconsuming, and subjective, and may also be unhelpful due to the consumerproviding subjective and inaccurate information to the dietitian, giventhat consumers notoriously overestimate their healthy habits andunderestimate their unhealthy habits.

SUMMARY

In one aspect of the disclosure, a method for customizing reciperecommendations may include receiving a target number of calories for auser, receiving physical movement data of the user from one or moreelectronic sensors configured to directly measure physical movement ofthe user, analyzing the received physical movement data, determining oneor more physical movement parameters based on the analysis of thereceived physical movement data, determining the recentness of each ofthe recipes being recommended or logged to the user, assigning a weightto each of the recipes based on the received target number of caloriesfor the user, the determined one or more physical movement parameters,and the determined recentness of the recipe being recommended or loggedto the user, ranking the recipes based on their assigned weights, andgenerating a custom recipe recommendation for the user based on theranking of the recipes.

Another aspect of the disclosure may include any combination of theabove-mentioned features and may further include the determined one ormore physical movement parameters including number of calories burned bythe user.

Another aspect of the disclosure may include any combination of theabove-mentioned features and may further include the one or moreelectronic sensors including a wearable electronic sensor configured tobe worn on a wrist of the user.

Another aspect of the disclosure may include any combination of theabove-mentioned features and may further include the one or moreelectronic sensors including an exercise machine electronic sensor.

Another aspect of the disclosure may include any combination of theabove-mentioned features and may further include the one or moreelectronic sensors including a sleep electronic sensor configured to bepositioned in a bed of the user.

Another aspect of the disclosure may include any combination of theabove-mentioned features and may further include the determined one ormore physical movement parameters including a sleep quality experiencedby the user.

Another aspect of the disclosure may include any combination of theabove-mentioned features and may further include the method furtherincluding receiving a cuisine preference of the user and the assigningof the weight to each of the recipes being further based on the receivedcuisine preference of the user.

Another aspect of the disclosure may include any combination of theabove-mentioned features and may further include the method furtherincluding receiving a diet preference of the user and the assigning ofthe weight to each of the recipes being further based on the receiveddiet preference of the user.

Another aspect of the disclosure may include any combination of theabove-mentioned features and may further include the method furtherincluding receiving an allergy status of the user and the assigning ofthe weight to each of the recipes being further based on the receivedallergy status of the user.

Another aspect of the disclosure may include any combination of theabove-mentioned features and may further include the method furtherincluding receiving a meat preference of the user and the assigning ofthe weight to each of the recipes being further based on the receivedmeat preference of the user.

Another aspect of the disclosure may include any combination of theabove-mentioned features and may further include one or morenon-transitory computer-readable media storing one or more programs thatare configured, when executed, to cause one or more processors toperform the method for customizing recipe recommendations.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings illustrate various embodiments of the presentmethod and system and are a part of the specification. The illustratedembodiments are merely examples of the present system and method and donot limit the scope thereof.

FIG. 1 is a diagram of an example health system;

FIGS. 2A-2B are example webpages of an example website that may beemployed in connection with the example health system of FIG. 1; and

FIGS. 3A-3B are a diagram of an example method for customizing reciperecommendations.

Throughout the drawings, identical reference numbers designate similar,but not necessarily identical, elements.

DETAILED DESCRIPTION

Methods for customizing recipe recommendations are disclosed herein.Specifically, the present methods generate custom recipe recommendationsfor users based on various data that is received or determined. Forexample, the received data may include a target number of calories for auser and physical movement data of the user. The received physicalmovement data may be received from one or more electronic sensorsconfigured to directly measure physical movement of the user. Thisreceived physical movement data may then be analyzed and then one ormore physical movement parameters may be determined based on theanalysis of the received physical movement data. Further, the recentnessof each of the recipes being recommended or logged to the user may bedetermined. A weight may then be assigned to each of the recipes basedon the target number of calories for the user, the determined one ormore physical movement parameters, and the determined recentness of therecipe being recommended or logged to the user. The recipes may then beranked based on their assigned weights. Finally, the custom reciperecommendation for the user may be generated based on the ranking of therecipes. The methods for customizing recipe recommendations aredescribed in detail below.

FIG. 1 is a diagram of an example health system 100. The system 100 mayinclude a server 102 that hosts a website 200. The system 100 may alsoinclude a laptop computer 104, a smartphone 106, a treadmill 108, and anactivity tracker watch 110 configured to be worn on the wrist of a firstuser 112. The system 100 may further include a desktop computer 114, atablet 116, a bicycle 118, and smart glasses 120 configured to be wornby a second user 122. The system 100 may also include a smart panel 124of a smart home network, a smart watch 126, a smart bed 128, and smartshoes 130 configured to be worn on the feet of a third user 132.

As disclosed in FIG. 1, each of the computing devices in the system 100may be configured to communicate with one another wirelessly, eitherlocally or remotely via a network 134. In particular, the activitytracker watch 110 worn by the first user 112 may include an electronicsensor, such as an accelerometer, that is configured to directly measurephysical movement of the first user 112, such as the number of stepstaken by the first user 112 or tracking movement of the first user 112while in bed, resulting in physical movement data. Similarly, thetreadmill 108 may include multiple electronic sensors, such as anodometer, a tilt sensor, and a resistance sensor, that are configured todirectly measure physical movement of the first user 112, such as thesimulated distance run by the first user 112 on the treadmill 108, theincline while running, and the amount of effort expended by the firstuser 112 on the treadmill 108, resulting in physical movement data. Thephysical movement data from the activity tracker watch 110 and thetreadmill 108 may be sent to, and received by, the laptop computer 104,the smartphone 106, or the server 102, or some combination thereof. Asoftware application running on the laptop computer 104, the smartphone106, or the server 102, or some combination thereof, may then beconfigured to analyze the physical movement data and then determine,based on the analysis of the physical movement data, one or morephysical movement parameters. These one or more physical movementparameters may include a number of calories burned by the first user112. After the software application has determined the one or morephysical movement parameters, the software application may then generatea custom recipe recommendation for the first user 112 based at least inpart on the one or more physical movement parameters.

Further, the smart glasses 120 worn by the second user 122 may includemultiple electronic sensors, such as a GPS receiver and a video camera,that are configured to directly measure physical movement of the seconduser 122, such as the distance traveled and the amount of head movementby the second user 122, resulting in physical movement data. Similarly,the bicycle 118 may include an electronic sensor, such as a cadencesensor, that is configured to directly measure physical movement of thesecond user 122, such as the number of pedal strokes performed by thesecond user 122 on the bicycle 118, resulting in physical movement data.The physical movement data from the smart glasses 120 and the bicycle118 may be sent to, and received by, the desktop computer 114, thetablet 116, or the server 102, or some combination thereof. A softwareapplication running on the desktop computer 114, the tablet 116, or theserver 102, or some combination thereof, may then be configured toanalyze the physical movement data and then determine, based on theanalysis of the physical movement data, one or more physical movementparameters. After the software application has determined the one ormore physical movement parameters, the software application may thengenerate a custom recipe recommendation for the second user 122 based atleast in part on the one or more physical movement parameters.

Also, the smart shoes 130 worn by the third user 132 may includemultiple electronic sensors, such as insole-mounted motion sensors, thatare configured to directly measure physical movement of the third user132, such as movement of the feet of the third user 132, resulting inphysical movement data. Similarly, the smart bed 128 may include anelectronic sensor, such as a sleep sensor positioned in the smart bed128, that is configured to directly measure physical movement of thethird user 132, such as tracking movement of the third user 132 while inthe smart bed 128, resulting in physical movement data. Further, thesleep sensor may be configured to track the temperature of the roomwhile the third user 132 is in the smart bed 128. The physical movementdata from the smart shoes 130 and the smart bed 128 may be sent to, andreceived by, the smart panel 124, the smart watch 126, or the server102, or some combination thereof. A software application running on thesmart panel 124, the smart watch 126, or the server 102, or somecombination thereof, may then be configured to analyze the physicalmovement data and then determine, based on the analysis of the physicalmovement data, one or more physical movement parameters. These one ormore physical movement parameters may include a sleep qualityexperienced by the third user 132. After the software application hasdetermined the one or more physical movement parameters, the softwareapplication may then generate a custom recipe recommendation for thethird user 132 based at least in part on the one or more physicalmovement parameters.

FIGS. 2A-2B are example webpages of the website 200 that may be employedin connection with the system 100 of FIG. 1.

As disclosed in FIG. 2A, a first webpage 210 of the website 200 may beconfigured to be presented to a user in order to receive data about theuser. In particular, the first webpage 210 may be configured to receivethe user's birthday, height, sex, current weight, and weight loss goalin data entry fields 212-220, respectively.

As disclosed in FIG. 2B, a second webpage 230 of the website 200 may beconfigured to be presented to a user in order to receive data regardingthe preferences of the user. In particular, the second webpage 230 maybe configured to receive the user's target number of calories, cuisinepreference, diet preference, allergy status, and meat preference in dataentry fields 232-240, respectively.

FIGS. 3A-3B are a diagram of an example method 300 for customizingrecipe recommendations. The method 300 may be performed, for example, bya software application being executed on the server 102, the laptopcomputer 104, the smartphone 106, the desktop computer 114, the tablet116, the smart panel 124, or the smart watch 126, or some combinationtherefore, of FIG. 1.

The method 300 may include receiving, at 302, a target number ofcalories for a user, a cuisine preference of the user, a diet preferenceof the user, an allergy status of the user, and a meat preference of theuser.

The method 300 may include receiving, at 304, physical movement data ofthe user from one or more electronic sensors configured to directlymeasure physical movement of the user.

The method 300 may include analyzing, at 306, the received physicalmovement data.

The method 300 may include determining, at 308, one or more physicalmovement parameters based on the analysis of the physical movement data.The determined one or more physical movement parameters may include anumber of calories burned by the user.

The method 300 may include determining, at 310, the recentness of eachof the recipes being recommended or logged to the user.

The method 300 may include assigning, at 312, a weight to each of therecipes based on the received target number of calories for the user,the received cuisine preference of the user, the received dietpreference of the user, the received allergy status of the user, and thereceived meat preference of the user, the determined one or morephysical movement parameters, and the determined recentness of therecipe being recommended or logged to the user.

The method 300 may include ranking, at 314, the recipes based on theirassigned weights.

The method 300 may include generating, at 316, a custom reciperecommendation for the user based on the ranking of the recipes.

INDUSTRIAL APPLICABILITY

In general, the methods for customizing recipe recommendations disclosedabove generate custom recipe recommendations for users based on variousdata that is received or determined. Various modifications to themethods disclosed above will now be disclosed.

The software application disclosed herein that is configured to receivedata, analyze data, make determinations with respect to data, andgenerate custom recipe recommendations may be configured to be executedon one or more computing devices. For example, the computing devices mayinclude, but are not limited to, an application or app that is executedon a smartphone, a smart watch, a smart panel of a smart home network,an exercise machine, a laptop computer, a tablet, or a desktop computer.Further, the software application may be distributed across two or morecomputing devices that communicate with each other over a wired orwireless network.

Further, the software application disclosed herein may be configured toexecute according to one or more formulas. For example, the weightassigned to each recipe by the software application disclosed herein maybe calculated according to the following formula:A*B*C*D*E*F*G*H=Recipe Weightwhere:

A=Recently logged recipe weight

B=Recently recommended recipe weight

C=Target calorie weight

D=Cuisine weight

E=Diet weight

F=Allergen weight

G=Preferred meat weight

H=Poor sleep adaptation weight

Using this formula, a weight=1 may be considered neutral, a weight>1 maybe preferred, a 0<weight<1 may not be preferred but allowed, and aweight=0 may not be allowed and never recommended. Since the weights forall different preference settings in this formula are multipliedtogether for each recipe, if a recipe has a total weight of 1.2, it istwice as likely to be recommended as a recipe that has a weight of 0.6.For example, where a recipe has not been logged in the past 10 days (1),but was recommended four days ago (0.8704), falls within the allowedcalorie range (1), is a preferred cuisine type selected by the user(1.5), falls in the diet the user has selected their goal (1), has noallergens (1), uses a meat that is not preferred (0.2), and the user hada good night's rest (1), the weight for the recipe will be1*0.8704*1*1.5*1*1*0.2*1=0.26112. With a weight of 0.26112, this weightis far less likely to be recommended than average. Calculations of eachof the individual weights A-H will now be described.

The following formula may be used to calculate the weight A, whichaffects how often a recipe will be recommended again after it has beenlogged.1−(0.85{circumflex over ( )}(t−1))=AThis formula assumes that that t represents a number of days, with t=0on the day the recipe is logged, and this formula is only employed wheret≤10. For example, if a user logged the recipe six days ago, the weightA will be 1−(0.85{circumflex over ( )}(6−1))=0.5563. Once t=10, theweight A=1.

The following formula may be used to calculate the weight B, whichaffects how often a recipe will be recommended again after it has beenrecommended but not logged.1−(0.6{circumflex over ( )}t)=BThis formula assumes that t represents a number of days, t=0 on the daythe recipe is recommended but not logged, and this formula is onlyemployed where t≤5. For example, if a user was recommended the recipethree days ago but the recipe was not logged, the weight B will be1−(0.85{circumflex over ( )}3)=0.784. Once t=5, the weight B=1.

The weight C may affect how often a recipe will be recommended based ona target calorie intake. For example, if a recipe is more or less thanthe target calorie intake by 50 calories, the weight C=0, otherwise theweight C=1. For example, if the target number of calories for the useris 450 calories, recipes from 400 to 500 calories will have a weight C=1, and all other recipes will have a weight C=0.

The weight D may affect how often a recipe will be recommended based onif the recipe is the preferred cuisine type of a user. For example, theweight D=1.5 if the recipe falls within the user's preferred cuisine,and the weight D=1 if the recipe falls outside the user's preferredcuisine.

The weight E may affect whether a recipe will be recommended based on ifthe recipe fits within a user's specified diet. For example, if no dietis selected by the user, the weight E=1 for all recipes. If a diet isselected by the user, the weight E=1 if the recipe falls within theselected diet and the weight E=0 if the recipe does not fall within theselected diet.

The weight F may affect whether a recipe will be recommended based on ifthe recipe includes foods to which the user is allergic. For example,the weight F=1 if the recipe does not include any foods to which theuser is allergic and the weight F=0 if the recipe does include any foodto which the user is allergic.

The weight G may affect how often a recipe will be recommended based onif the recipe includes meats that the user prefers. For example, if nomeat preference is selected by the user, the weight G=1 for all recipes.If a meat preference is selected by the user, the weight G=1 if therecipe includes meats the user prefers and the weight G=0.2 if therecipe includes other meats.

The weight H may affect how often a recipe will be recommended based onthe user's sleep quality during the night prior to the day of the reciperecommendation. For example, if the user had high quality sleep, theweight H=1 for all recipes. If the user had poor quality sleep, theweight H=2.0 if the recipe has a fiber content of more than 6 gramsand/or the recipe has 30% or more of its calories from protein,otherwise the recipe receives a weight H=0.5. A poor quality sleep maybe a sleep with a score less than or equal to 60 or a sleep time of lessthan six hours.

It is understood that the various weights A-H employed in the RecipeWeight formula above may be combined in a variety of ways includingeliminating one or more of the weights A-H from the Recipe Weightformula.

Further, the software application disclosed herein may include the useof a special-purpose or general-purpose computer, including variouscomputer hardware or software. The software application may beimplemented using non-transitory computer-readable media for carrying orhaving computer-executable instructions or data structures storedthereon. Such computer-readable media may be any available media thatmay be accessed by a general-purpose or special-purpose computer. By wayof example, and not limitation, such computer-readable media may includenon-transitory computer-readable storage media including RAM, ROM,EEPROM, CD-ROM or other optical disk storage, magnetic disk storage orother magnetic storage devices, or any other storage medium which may beused to carry or store one or more desired programs having program codein the form of computer-executable instructions or data structures andwhich may be accessed and executed by a general-purpose computer,special-purpose computer, or virtual computer such as a virtual machine.Combinations of the above may also be included within the scope ofcomputer-readable media. Computer-executable instructions comprise, forexample, instructions and data which, when executed by one or moreprocessors, cause a general-purpose computer, special-purpose computer,or virtual computer such as a virtual machine to perform a certainmethod, function, or group of methods or functions.

The communication between computing devices disclosed herein may beaccomplished over any wired or wireless communication network including,but not limited to, a Local Area Network (LAN), a Wide Area Network(WAN), a Wireless Application Protocol (WAP) network, a Bluetoothnetwork, an ANT network, or an Internet Protocol (IP) network such asthe Internet, or some combination thereof.

The receipt of data from a user disclosed herein in connection withvarious webpages of a website may additionally or alternatively beaccomplished using other data gathering technologies including, but notlimited to, receiving data from a user via data entry interfaces of anapp on a smartphone or gathering data regarding a user by accessingdatabases that already store the desired data such as registrationdatabases of an app server or a website server, or some combinationthereof. Further, the receipt of data from a user disclosed herein inconnection with various webpages of a website is example data only, andother types and specificity of data may additionally or alternatively bereceived from a user.

The electronic sensors disclosed herein that are configured to directlymeasure physical movement of the user may include both portable as wellas stationary electronic sensors. Portable electronic sensors mayinclude, but are not limited to, electronic sensors built into smartwatches, fitness trackers, sport watches, head mounted displays, smartclothing, smart jewelry, vehicles, sports equipment, or implantablesconfigured to be implanted in the human body, or some combinationthereof. Stationary electronic sensors may include, but are not limitedto, sensors built into exercise machines, furniture, beds or bedding (tomeasure physical movement while in bed and/or while asleep), flooring,walls, ceilings, doorways, or fixtures along paths and roadways, or somecombination thereof. These sensors configured to measure physicalmovement of the user may include, but are not limited to, sensors thatmeasure physical movement using infrared, microwave, ultrasonic,tomographic, GPS, accelerometer, gyroscope, odometer, tilt, speedometer,piezoelectric, or video technologies, or some combination thereof.

The use of one or more electronic sensors in the example methodsdisclosed herein may solve the problem of a subjective recommendationfrom a dietitian that is based on subjective information provided by auser. In particular, since a dietitian is a human being, the dietitianis inherently biased and any recommendations are necessarily subjectiveinstead of objective. Further, there are severe limitations to whattypes of information, and accuracy of information, that a human user cangather and convey to the human dietitian. The use of one or moreelectronic sensors in the example methods disclosed herein may solvethese problems by using highly sophisticated and specialized electronicsensors that are configured to objectively and directly measure physicalmovement of the user resulting in objective physical movement data andthen sending that objective physical movement data to the objectivesoftware application disclosed herein instead of a subjective humandietitian. These electronic sensors may have specific tolerances and mayenable a single computing device to measure multiple users in multipleremote locations. None of these capabilities are available to a humanuser absent these highly sophisticated and specialized electronicsensors. These highly sophisticated and specialized electronic sensorsmay therefore solve the problems with the prior art method byobjectively and accurately measuring physical movement of the userinstead of relying on subjective and biased observations of a user.

Further, the example methods disclosed herein are not directed to anabstract idea because they solve a technical problem using highlysophisticated and specialized electronic sensors. The data generated bythese electronic sensors simply has no equivalent to pre-electronicsensor, manual paper-and-pencil data.

Also, the example methods disclosed herein may improve the technicalfield of automated recipe recommendations. For example, the technicalfield of automated recipe recommendations may be improved by the examplemethods disclosed herein at least because the prior art method did notenable the automatic measurement of the physical movement of a user andthe automatic sending of physical movement data to a softwareapplication capable of customizing a recipe recommendation based on anautomatic analysis and determination of parameters from the receivedphysical movement data.

What is claimed is:
 1. A method for customizing recipe recommendations,the method comprising: receiving, at a server, a target number ofcalories for a user, entered on a webpage of a website hosted at theserver; receiving, at the server, physical movement data of the userfrom one or more electronic sensors configured to directly measurephysical movement of the user and configured to wirelessly transmit thephysical movement data to the server over a network; analyzing, at theserver, the received physical movement data; determining, at the server,one or more physical movement parameters based on the analysis of thereceived physical movement data; determining, at the server, arecentness of each of multiple recipes being recommended or logged tothe user; assigning, at the server, a weight to each of the recipesbased on the received target number of calories for the user, thedetermined one or more physical movement parameters, and the determinedrecentness of the recipe being recommended or logged to the user;ranking, at the server, the recipes based on their assigned weights; andgenerating, at the server, a custom recipe recommendation for the userbased on the ranking of the recipes.
 2. The method of claim 1, whereinthe determined one or more physical movement parameters include a numberof calories burned by the user.
 3. The method of claim 2, wherein theone or more electronic sensors are included in a wearable activitytracker configured to be worn on a wrist of the user.
 4. The method ofclaim 2, wherein the one or more electronic sensors includes an exercisemachine electronic sensor.
 5. The method of claim 1, wherein the one ormore electronic sensors includes a sleep electronic sensor configured tobe positioned in a bed of the user.
 6. The method of claim 5, whereinthe determined one or more physical movement parameters include a sleepquality experienced by the user.
 7. The method of claim 1, wherein: themethod further comprises receiving, via the webpage of the websitehosted at the server, a cuisine preference of the user; and theassigning, at the server, of the weight to each of the recipes isfurther based on the received cuisine preference of the user.
 8. Themethod of claim 1, wherein: the method further comprises receiving, viathe webpage of the website hosted at the server, a diet preference ofthe user; and the assigning, at the server, of the weight to each of therecipes is further based on the received diet preference of the user. 9.The method of claim 1, wherein: the method further comprises receiving,via the webpage of the website hosted at the server, an allergy statusof the user; and the assigning, at the server, of the weight to each ofthe recipes is further based on the received allergy status of the user.10. The method of claim 1, wherein: the method further comprisesreceiving, via the webpage of the website hosted at the server, a meatpreference of the user; and the assigning, at the server, of the weightto each of the recipes is further based on the received meat preferenceof the user.
 11. A method for customizing recipe recommendations, themethod comprising: receiving, at a server, a target number of caloriesfor a user, a cuisine preference of the user, a diet preference of theuser, an allergy status of the user, and a meat preference of the user,entered on a webpage of a website hosted at the server; receiving, atthe server, physical movement data of the user from one or moreelectronic sensors configured to directly measure physical movement ofthe user and configured to wirelessly transmit the physical movementdata to the server over a network; analyzing, at the server, thereceived physical movement data; determining, at the server, one or morephysical movement parameters based on the analysis of the receivedphysical movement data, the determined one or more physical movementparameters including a number of calories burned by the user;determining, at the server, a recentness of each of multiple recipesbeing recommended or logged to the user; assigning, at the server, aweight to each of the recipes based on the received target number ofcalories for the user, the received cuisine preference of the user, thereceived diet preference of the user, the received allergy status of theuser, and the received meat preference of the user, the determined oneor more physical movement parameters, and the determined recentness ofthe recipe being recommended or logged to the user; ranking, at theserver, the recipes based on their assigned weights; and generating, atthe server, a custom recipe recommendation for the user based on theranking of the recipes.
 12. The method of claim 11, wherein the one ormore electronic sensors are included in a wearable activity trackerconfigured to be worn on a wrist of the user and configured to count thesteps of the user.
 13. The method of claim 11, wherein the one or moreelectronic sensors includes an exercise machine electronic sensorconfigured to track an amount of effort expended by the user on theexercise machine.
 14. The method of claim 11, wherein the one or moreelectronic sensors includes a sleep electronic sensor configured to bepositioned in a bed of the user and configured to track movement of theuser while in the bed.
 15. The method of claim 14, wherein thedetermined one or more physical movement parameters include a sleepquality experienced by the user.
 16. One or more non-transitorycomputer-readable media storing one or more programs that areconfigured, when executed, to cause one or more processors to perform amethod for customizing recipe recommendations, the method comprising:receiving, at a server, a target number of calories for a user, acuisine preference of the user, a diet preference of the user, anallergy status of the user, and a meat preference of the user, enteredon a webpage of a website hosted at the server; receiving, at theserver, physical movement data of the user from one or more electronicsensors, the one or more electronic sensors configured to directlymeasure physical movement of the user and configured to wirelesslytransmit the physical movement data to the server over a network;analyzing, at the server, the physical movement data; determining, atthe server, one or more physical movement parameters based on theanalysis of the physical movement data, the determined one or morephysical movement parameters including a number of calories burned bythe user; determining, at the server, a recentness of each of multiplerecipes being recommended or logged to the user; assigning, at theserver, a weight to each of the recipes based on the received targetnumber of calories for the user, the received cuisine preference of theuser, the received diet preference of the user, the received allergystatus of the user, and the received meat preference of the user, thedetermined one or more physical movement parameters, and the determinedrecentness of the recipe being recommended or logged to the user;ranking, at the server, the recipes based on their assigned weights; andgenerating, at the server, a custom recipe recommendation for the userbased on the ranking of the recipes.
 17. The one or more non-transitorycomputer-readable media of claim 16, wherein the one or more electronicsensors are included in a wearable activity tracker configured to beworn on a wrist of the user and configured to count the steps of theuser and configured to track movement of the user while in bed.
 18. Theone or more non-transitory computer-readable media of claim 16, whereinthe one or more electronic sensors includes an exercise machineelectronic sensor configured to track an amount of effort expended bythe user on the exercise machine and configured to track a simulateddistance traveled by the user on the exercise machine.
 19. The one ormore non-transitory computer-readable media of claim 16, wherein the oneor more electronic sensors includes a sleep electronic sensor configuredto be positioned in a bed in a room of the user and configured to trackmovement of the user while in the bed and track a temperature of theroom while the user is in the bed.
 20. The one or more non-transitorycomputer-readable media of claim 19, wherein the determined one or morephysical movement parameters include a sleep quality experienced by theuser.